Computer Vision & Video Analytics

The first crowd-counting system homologated by the Spanish Metrology Center, with >99% accuracy and real-time multi-camera fusion.

The problem

Certifiably accurate crowd counting in massive venues (trade fairs, casinos, street festivals), where a single camera can’t cover wide entrances and overlap between cameras causes double counting.

Stack decision

Python prototype (YOLOv2 + a custom tracking algorithm) rewritten in C++ with CUDA to scale performance; multithreading to process many cameras on a single GPU.

The technical challenge

Fusing and synchronizing up to 12 aligned cameras in real time into a single view where analytics run, with fine overlap configuration so the same person is never counted twice.

Impact

Over 99% accuracy homologated by the Spanish Metrology Center — the first certified counting system. Robust with up to 200 people under a single camera (San Fermín festival).

Gesture-based highlight detection and player heatmaps from each match’s footage.

The problem

Automatically extracting a match’s best moments and player analytics from already-recorded footage, optimizing cost and processing time.

The technical challenge

Telling the chosen highlight gesture (raised arms) apart from similar in-game moves (smashes, volleys): an algorithm over the joints (YOLO Pose) that catches true positives without adding false ones.

Impact

Processed with GPU workers on RunPod to optimize cost and time, with a strong focus on multithreading.

Turnstile counting, fraud detection and platform occupancy deployed across the entire Renfe network.

The problem

Monitoring accesses and platforms across an entire rail network: turnstile counting, fraud (two people at once, jumping the gate), people carrying objects, track intrusions and occupancy.

Impact

Deployed across the whole Renfe network and periodically audited by the client, recurrently validating its accuracy.

Final degree project: a 3D camera and a robotic arm that detects an object, approaches it and grabs it.

The problem

Assembling a 3D camera with a motorized robotic arm and a vision algorithm to detect an object and its distance, follow it, approach and grab it automatically.

The technical challenge

The 3D camera loses depth below 0.5 m; solved by interpolating with wheel odometry to estimate position in the near range, plus the arm’s inverse kinematics.